Development of a Residual Biomass Supply Chain Simulation Model Using AnyLogistix: A Methodical Approach

Abstract

Background: In the pursuit of sustainable energy sources, residual biomass has emerged as a promising renewable resource. However, efficiently managing residual biomass poses significant challenges, particularly in optimizing supply chain operations. Advanced modeling approaches are necessary to address these complexities. This study aims to develop a comprehensive methodological framework for creating simulation models tailored to agroforestry residual biomass supply chains. Methods: The study employs a hybrid simulation approach, integrating geographic information system mapping with a case study analysis. The simulation was conducted over a 365-day period, using the anyLogistix software (version 2.15.3.202209061204) to model various supply chain dynamics. The framework also accounts for financial, operational, customer satisfaction, and environmental metrics. Results: The simulation results showed a total expenditure of EUR 5,219,411.3, with transportation being the primary cost driver, involving 5678 trips and a peak capacity of 67.16 m³. CO₂ emissions were measured at 487.7 kg/m³. The model performed as expected, highlighting the need for sustainable logistics strategies to reduce costs, lower losses, and improve productivity. Conclusions: This study presents one of the first detailed methodological frameworks for simulating agroforestry residual biomass supply chains. It provides valuable managerial insights into the financial, operational, and environmental aspects of supply chain management. The findings may stakeholders make informed decisions to enhance the sustainability of biomass utilization in energy production.

1. Introduction

In alliance with the 7th sustainable development goals, utilization of residual biomass has resuscitated the hopes of achieving a clean and affordable energy. A reliable and environmentally sustainable energy supply chain can be developed by utilizing forestry waste, organic waste, and agricultural residues [1]. The operational logistics for residual biomass originating from agricultural and forest sources often involve intricate networks of supply chains [2]. The decision adopted after considering several aspects of the system that have an impact on the effectiveness of the biomass supply chain greatly influences the performance of a supply chain [3]. An analysis of a few critical factors, including the availability of biomass, collection logistics, storage, conversion technologies, and the customer demand, is necessary for the design of an efficient supply chain [4]. The functionality of a residual biomass supply chain can be precisely assessed using a simulation model [5]. In most cases, simulation modeling is very beneficial, allowing stakeholders to evaluate, test, and fine-tune various scenarios prior to execution [6,7,8].

Prior to the advent of simulation, traditional methods for optimizing agroforestry residual biomass supply chains often relied on heuristic approaches, mathematical modeling, and manual optimization techniques. These traditional methods provided valuable insights and solutions for optimizing biomass supply chains, but they often struggled to address the intricacies, uncertainties, and dynamic nature of modern supply chain environments [9,10]. To tackle the problem, this study aims to lay out an exhaustive methodological framework for planning and implementing a simulation model for a residual biomass supply chain using anyLogstix simulation software, with the intention of having an insight to the behavior of the system in real time. This study focuses on the following research questions and intends to answer them:

• In what ways does simulation modeling improve the efficiency and reliability of residual biomass supply chains compared to traditional optimization methods?
• What role does anyLogistix simulation software play in analyzing and optimizing the performance of a residual biomass supply chain?
• What key performance indicators (KPIs) should be used to evaluate the financial, operational, customer satisfaction, and environmental metrics of a residual biomass supply chain?
• How can the simulation model designed for residual biomass supply chains be applied to real-world case studies, and what insights can be gained from empirical analysis?

The remaining part of this article is structured as follows: Section 2 presents a review of past work carried out within the scope of the study and the novelty of the study. In Section 3, a detailed description of the methodology and some salient terminologies are presented. Section 4 defines the case study, data collection procedures for the supply chain entities, and the model structure and implementation. Section 5 presents the results of the key performance indicators considered in the simulation experiment. Section 6 presents the result analysis, managerial insights, societal impacts of the study, contributions of the study, and recommended future work. Section 7 presents the conclusion and major contributions of the study.

2. Literature Review

This section provides a presentation and brief discussion of past work carried out within the scope of the study.

To establish sustainability-driven decisions for biomass supply chains, Jazinaninejad et al. proposed key tools for quantitative analytics [11]. This study uses a rule-governed content and qualitative analysis approach along with deductive and inductive approaches to perform a systematic assessment of 450 peer-reviewed papers. They concluded that municipal solid waste sources might be further investigated, while energy crops and biomass derived from forests are the most treated biomass feed stock categories. A thorough classification of the approaches used in this subject that considers the influence of policy processes in managerial decisions is, however, lacking in the literature.

In their work, Ahmadvand and Sowlati improved the supply chain of biomass acquired from forestry residues used to produce syn-gas while taking uncertainties into account [12]. A strong optimization model that adopts Monte Carlo simulation is created and used in the actual situation of a sizable Kraft pulp mill in British Columbia, Canada. The resilient model provides a singular, practical solution that remains valid across the entire range of parameter values within their associated uncertainty intervals. Notably, even though the total supply chain cost in the case study using the robust optimization model is 67% higher than that of the deterministic model, this robust solution stands as a viable choice.

Aalto M et al. examined the use of three simulation modeling methods to study the biomass supply chain, which is a complex logistics system composed of discrete processes distributed in space [13]. The authors drew the conclusion that integrating modeling approaches have significant potential for including additional variables, improving outcomes, and assisting academics, decision makers, and operation managers by generating more trustworthy data. They, however, could not find an article that tackled all three simulation techniques under study.

Nunes and Silva investigated important facets of managing biomass, including gathering, moving, storing, and processing it, as well as how these factors affect the overall expense [14]. The study investigates how the effectiveness and costs of the supply chain are impacted by variables such as seasonal fluctuations and quality variations in biomass. Using mathematical optimization models, the research thoroughly analyzes these variables, allowing for the investigation of various situations and optimization techniques. To offer thorough insights, these models incorporate methods like tabu search, genetic algorithms, and linear programming to achieve cost optimization. Although this work is closely related to our thematic area, the simulation approach is not specifically improving the residual biomass supply chain.

The methodology applied in the work of Rijal et al. involves the mapping of the supply chain for residual forestry biomass to gain a thorough understanding of its material, financial, and informational fluxes [15]. Economic aspects like expenses and difficulties are also examined. The authors use a blend of qualitative, quantitative, and case study methodologies, including economic analysis and supply chain-management-specific modeling approaches, data collecting, and analysis. This work differs from ours in that we adopt a methodological approach to identifying the key performance indicators to improve the supply chain.

In their study, Hong, Boon, Bing, and Hon employ a methodology consisting of the summary and analysis of the biomass-to-bioproduct supply chain [16]. It gives an overview of techniques for synthesizing and optimizing biomass supply chains, specifies the traits of a sustainable integrated biomass supply chain, and methodically explains the decisions and obstacles involved in this chain. To shed light on the idea of sustainable biomass supply chain management and optimization, the strategy is that of a thorough literature review. It also identifies important obstacles and possible directions for further study.

To improve the agri-biomass supply chain management and feed stock supply, Wu et al. establish a multidisciplinary strategy that includes operational research, geographic information systems, mathematical modeling, technical economic analysis, and sensitivity analysis [17]. In their findings, they demonstrated that, in comparison to the real scenario in Shandong Province in Chia, the agri-biomass supply chain is economical. The results of the sensitivity analysis showed that the ideal agri-biomass supply chain architecture was susceptible to adjustments. The method used was analytical, while we focused on a simulation model.

In another study, Wang and Yao conducted an exhaustive peer review of research works that were aimed at improving biomass supply chain management in relation to these three different types of biomass sources [18]. The works were categorized using a framework that uses problem-based and methodology-based methods. The findings demonstrate the use of contemporary technical methods and tools for management-related issues. It is clear from the assessment that the current trends in biomass supply chain management and the potential for future application of cutting-edge approaches play a key role in the economic and sustainability effectiveness of the biomass supply chain.

To provide the least amount of biomass and transportation expenses to produce electricity in power plants, in their study, Dangprok, Tippayawong, and Tippayawong aimed to design an optimization model that can be used as a tool to assess different biomass materials and their sources before production [2]. A cost optimization model for the Thailand scenario was created by considering the cost of biomass and the cost of transportation. Ten agricultural residual biomass materials that were most prevalent in Thailand were understudied. The results show that it was possible to reduce the overall costs by more than 50%. These findings were based solely on the cost of biomass and transportation, without considering seasonal variations or the constraints of certain processes. The finalized total cost may fluctuate depending on the provided parameters.

In their paper, Zailan et al. analyzed the performance of the technologies and difficulties faced in providing the future and actual situation of co-generation systems in Malaysia [19]. A quantitative content-based analysis and meta-analysis were used to prepare this paper. A complicated optimization model that incorporates biomass-based co-generation and the biomass supply chain while taking economic and environmental sustainability into account is made possible by the practical application of this review. It will speed up the Malaysian “Industry 4.0-driven” energy strategy even more.

In their work, Sharma, Ingalls, Jones, and Khanchi present a thorough analysis of the architecture and modeling of the biomass supply chain (BSC) before carefully describing the energy requirements, energy targets, feed stocks for biofuels, and conversion processes [20]. This article presents a thorough analysis of mathematical programming models created for the BSC and indicates the major difficulties and viable directions for further research. The review’s extensive analysis of the BSC modeling and design will help researchers obtain the foundation for understanding biomass feedstocks and the generation of biofuels.

Using the lean concept, the authors created a novel information management model based on a web application for agroforestry residual biomass supply chains [21]. The research takes a hybrid approach, incorporating theoretical frameworks, expert interviews, and literature studies. This study is aimed at creating an effective model for waste reduction.

The literature accessed in this review highlights various aspects of biomass supply chain management, including feedstock categories, modeling techniques, optimization models, cost analysis, spatial considerations, and sustainability factors. However, detailed work on an agroforestry residual biomass supply chain simulation model is lacking. Also, simulating the supply chain using anyLogistix for agroforestry residual biomass was lacking too. Hence, the novelty of this study lies in its use of a dynamic simulation model specifically built in anyLogistix to evaluate the strategies and operational tactics for the residual biomass supply chain. Unlike previous studies, which may have focused on static models or specific aspects of biomass logistics, this research integrates comprehensive financial, operational, environmental (CO₂ emissions), and customer satisfaction metrics into a dynamic framework. It also incorporates real-world data from a Portuguese logging firm and considers both direct and distribution-center-based supply flows, allowing for a more holistic evaluation of the supply chain’s performance, including the impact of transportation costs and environmental sustainability. This integrated, agent-based approach offers a new perspective on optimizing the biomass supply chain for efficiency and sustainability.

3. Methodology

The aim of this work is to develop simulation models in a specific simulator with the integration of optimization models to support decisions in the dynamic evaluation of strategies and tactics of the operation of the residual biomass supply chain with agricultural and forest origins. The implementation is classified into the following steps:

• Define the objectives of the simulation models.
• Data collection for each component of the agroforestry biomass supply chain.
• Model design and structure (hybrid simulation approach).
• Case study implementation
• Simulation experiment.
• Decision making.

The flow chart of the methodological procedure used in the study is illustrated in Figure 1.

3.1. Model Design and Structure

The scope of the simulation model is structured to incorporate the entire supply chain for the agroforestry residual biomass. The model begins with the raw materials’ acquisition and storage, procurement, transport logistics, and distribution. Analysis of the time, cost, emissions, and energy consumption related to each of these tasks are supplied while taking the system’s dynamics into account. The model was designed using anyLogistix software version: 2.15.3.202209061204.

AnyLogistix is a decision-oriented software comprising user-defined tables and logic, designed specifically for supply chain design and optimization. It solves various decision-making issues in a real-world logistics system using two technological pillars: optimization and system simulation capabilities [22]. Metrics are evaluated based on types (supplier, customer, distribution center, factory), object (factories, individual distribution centers, customers, suppliers), and products (individual products). The supply chain entities are designed in relation to their properties and interaction with other entities within the scope of the supply chain under study. The simulation model utilizes GIS visualizations and agent-based, discrete event, and system dynamics in the development of the model.

3.2. Supply Chain Factors and Entity

The supply chain entities include suppliers, customers, and distribution centers, while the factors are vehicles, products, periods, and the demand. Each entity’s attributes and interactions are defined based on their function in the supply chain.

Table 1. List of entities and their parameters.

Entity	Input Parameter	Key Performance Indicators
Suppliers	Name Location	Total cost Transportation cost
Sites (Distribution Centers and Factories)	Name Location Capacity	Inventory costs
Customer	Name Type Location	Service level Customer revenue Fulfillment order
Demand	Customer Product Time period Revenue Currency Expected lead time. Back-order policy	Expected lead time (ELT) Service level
Period	Name Start and End date.	Lead time
Products	Name Unit Cost Selling price Currency	Peak capacity
Vehicle	Name Capacity Capacity unit Speed Speed unit CO2 emission	Total CO2 emissions Transportation cost Number of vehicles used

shows a list of entities and factors, its salient input parameters, and the output parameters associated with it.

A brief description of the entities that are components of the supply chain follows:

• Supplier: An organization or function that supplies products, resources, or services to clients or other upstream entities is referred to as a supplier in a supply chain system. To satisfy consumer demand, the supplier serves as the origin or source of the goods or resources required. The location, name, and number of distribution centers define the supplier entity.
• Customer: An organization or position within a logistics system or supply chain that receives products or services from a provider or supplier is referred to as a customer. Depending on the situation, it may refer to a variety of entities, including people, businesses, or organizations. It is defined based on the name, type, and location of the customer.
• Distribution Center: Supply chains that hold completed goods prior to being selected and packaged to fulfill orders for customers are known as distribution centers.
• Transportation Cost: This is the cost calculated based on product volume, distance, and truck capacity. It involves a simple, yet effective, method to compute the cost per unit of transported biomass (in cubic meters) using a product and distance-based approach.

4. Case Study

To demonstrate the process, a study and simulation of the supply chain process adapted from the works of Brás and Rijal et al. [15,23] was implemented. The objective of this case study analysis is to develop simulation models in anyLogistix to support the decision in the dynamic evaluation of strategies and tactics of operation of the residual biomass supply chain.

4.1. Data Collection for Supply Chain Entities

The supply chain formation under study is comprised of a big logging firm (Logger Y) in Portugal that purchases and sells wood and forest residues. The firm doubles as a supplier and a distributor. Its feed stock is sourced from private forest owners and state-owned institutions. The logging firm also owns forest land and supplies residues to biomass generation plants. The customers in this supply chain are three independent energy companies and biomass generation plants (Company A, Company N, and Company C) located in the central region of Portugal.

Table 2. Supply chain entities and location.

	Name	Location
Customer (3)	Energy company A Power plant company N Power plant company C	Coimbra Coimbra Leiria
Distributor (2DCs)	Logger Y	Leiria (Monte Redondo) Coimbra (Soure)
Suppliers (3)	Forest landowners (FLO) State CMFF	Figueira da foz (Coimbra) Figueira da foz (Coimbra Leiria

presents the name and location of all entities in the supply chain under study and their corresponding locations.

The highlights of the background information about this case study are provided:

• Their supply chain offers one product (pellet).
• The supply chain is made up of three customers, two distribution centers (DCs), and three suppliers.
• Their supply chain runs at a 90% customer service level (CSL) policy.
• The distribution centers use an order on demand inventory control policy. The minimum level is 33.58 m³ subject to the customer service level of 90%. The maximum level is 514 312.03 subject to the maximum storage area capacity for each product at each distribution center.
• The customer expects to receive their order in a random time limit within intervals of 5 to 7 days.
• The demand is operating on an expected lead time (ELT) of 1 day.
• Trucks with a 95 m³ capacity transport products from the supplier to the distribution centers. Lorries with a capacity of 90 m³ transport products from the distribution centers to the customers. Both vehicle types travel a distance of 50 km.
• CO₂ produced from vehicles: truck = 19.8 g/m³/km, lorry = 17.5 g/m³/km. These values are deducted from the existing literature [24,25].
• CO₂ produced from a DC = 1.35 kg/m³. This value is adapted from the existing literature [24].
• Sourcing policy of FIFO (First-in First-out) FTL (Full Truck Load) shipments are used with a minimum load ratio of 0.9. A direct shipment distribution network is used.

4.2. Model Structure and Implementation

The case study is structured, implemented, and presented in this section. It provides information on the flow of materials between the supply chain entities. The input specifications predefined during the design and data collection stages are factored into the model.

The flow of materials from the suppliers to the logger and the customers is described in Figure 2, as adapted from Rijal et al. 2023 [14].

In this scenario, the distribution center managed by Logger Y and located in Soure obtains their supply of agroforestry residues from the forest landowners (FLOs) and the state covering an operating distance of 35.3 km, respectively, while from CMFF at an operating distance of 55.1 km. The customers, companies A and N, receive their supply directly from the suppliers at an operating distance of 27.6 km, respectively, or the distribution center as stipulated in the figure above.

As seen in the diagram in Figure 3, Company C can access their supplies from suppliers FLOs and state and from logger Y located at Monte Redondo. The logger, in turn, obtains its supplies from two suppliers. The operating distance between the suppliers and the distribution center is 45.8 km, while from the distribution center to the customer is 21.5 km.

As stated earlier in the previous section, the vehicle type, capacity, and speed specifications are presented in

Table 3. Vehicle specifications.

Type	Capacity (m3)	Speed (km/h)
Truck	95	50
Lorry	90	50

.

The cost of a full truck load ranges between EUR 540 and EUR 870. A full truck load of 95 m³ is equivalent to 25 tons (average of 20–30 tons). The logger at the different parks uses a lorry with capacity of 90 m³ to transport to the customers. The design for cost calculations utilizes the product and distance, which is calculated as cost per product measurement unit multiplied by volume and distance.

Where the cost per product measurement unit is the cost for transporting one item of product in the predetermined measurement unit to the distance of the unit as defined.

• Volume: the shipment’s entire value.
• Distance: the length of the trip.
• The cost of transporting one unit (m³) of the truck capacity in use is derived by dividing the total cost of transporting on full truck capacity by full truck capacity, see equations 1 and 2 for the reference.
• Where:
• sup>∙ C₁/m³ is the cost per unit for 95 m³ truck capacity;
• C₂/m³ is the cost per unit for 90 m³ lorry capacity.

$${\mathrm{C}}_1/{\mathrm{m}}^3={\displaystyle \frac{870}{95}}=9.15 €/{\mathrm{m}}^3$$

(1)

$${\mathrm{C}}_2/{\mathrm{m}}^3={\displaystyle \frac{870}{90}}=9.66 €/{\mathrm{m}}^3$$

(2)

The values derived from Equations (1) and (2) are the cost for transporting one metric ton of pellet from the source to the destination. These values are applied in the cost calculations for the simulation experiments. See Figure 4 for the implementation of the cost coefficient derived from the calculations in the path model element of the software.

5. Results

The variables defined in the scenario specified by the case study of logger Y are fixed into the various segments on the software. See Figure 5 for the Geographic Information System (GIS) map representation of the supply chain entities and their precise locations on the map as automatically generated in reference to their respective longitude and latitude.

The objects on the map represent agents, each following its unique behavior template. The legends of the objects are defined as follows:

• Blue circular icon with an A symbol: This represents the customer.
• Green circular icon with 3 interconnected arrows: This depicts the supplier.
• Red circular enclosure with a house icon inside it: This stands for the distribution centers of the supply chain.
• Blue lines: Connecting routes from one point to the other.

This template consists of a sequence of operations, which are discrete events. Communication between agents is integral to agent-based modeling. The simulation experiment was simulated for a period of 365 days, modeling the scenario defined in the case study and depicted in Figure 4. The results were then presented and analyzed according to predetermined key performance indicators.

The metrics analyzed in the study are filtered based on customer satisfaction, financial, operational, and environmental (CO₂ emissions) factor in correspondence to the predefined KPIs in Table 1. The subsequent figures show the plots, line graphs, and charts obtained from the experiment after running for a period of 365 days.

5.1. Total Cost

This metric is the total of all costs incurred during the period of analyzing the supply chain. Figure 6 is the line graph of the total cost incurred during the period of simulation.

The numerical value of the metric simulated is EUR 5 219,411.3. This value represents the total cost for 365 days. The high cost of transportation which accounts for the total costs is expected as stated in the empirical literature by Rijal et al.

5.2. Revenue by Customer

These data are generated by multiplying the quantity of orders made by all the customers with the selling price per product. In this situation, the data account for the 365 days the simulation experiment will run for; see Figure 7.

The line graph here depicts a steady and progressive increase in the revenue obtained from the customers in the supply chain with a numeric value of €14,352,654.73.

5.3. Number of Trips

Figure 8 is a measurement of the number of vehicles utilized during the period of the simulation. It can also be regarded as the number of trips expected to be made in the supply chain operational period. It is manually calculated as the ratio of the total demand ordered to the vehicle capacity per trip.

The simulation result gives a maximum of 5678 vehicles. This value translates to the number of trips embarked upon during the simulation period. This value, when compared to the number of vehicles on the trip (5380) calculated manually, does not differ so much, with an error of 5%.

5.4. Peak Capacity

The largest number of product items that have been held in stock during the simulation’s duration is referred to as peak capacity.

The numerical value as seen in the histogram chart in Figure 9 above is 67.16 m³. This implies that at every point in time during the one-year simulation period, the highest amount of residual biomass chips available in stock is approximately 67 m³.

This information helps the decision makers decide on the flow of orders with regard to volume and time.

5.5. Lead Time and Mean Time

The lead time is represented as the time it takes to deliver every ordered product item. It can be updated every time an order is sent out by any customer on the supply chain. The mean lead time, as the name implies, is the mean value of the total lead time; see Figure 10 for reference.

The blue line represents the lead time, while the red line represents the mean lead time. There were some inconsistencies in the lead time until after the 25th day, where it stabilizes at 10 days, while the estimated mean time is nine days. This KPI can assist the stakeholders in knowing how best to maximize operational performance on time to satisfy customers.

5.6. Expected Lead Time (ELT) Service Level

ELT service level by order is estimated as the ratio of orders delivered within the expected lead time to total orders fulfilled, while ELT service level by revenue demonstrates the quality of service by comparing the amount of money received from on-time orders to the total number of outbound orders. Figure 11 shows the superimposed linear (green) and time series (blue) graph for ELT service by revenue and ELT service by order, respectively.

As seen in the time series graph, the service level by order experienced a downward trend in the first 30 days of the simulation. Based on this information, decisions can be made to regulations in the first 40 days to stabilize the ELT service level by order.

5.7. Total CO₂ Emissions

This is the sum of all carbon emission types considered in the operations of the supply chain. Figure 12 presents the time linear graph of the total CO₂ emitted. This comprises of CO₂ from other sources and CO₂ from production.

The total CO₂ obtained includes the summation of the CO₂ from production and CO₂ from other causes. The numeric value is estimated at 487.7 kg/m³. Based on the output, to reduce the carbon effect on the environment, a decision to use more environmentally friendly vehicles can be utilized to drastically reduce the greenhouse gas emission in line with the sustainable developmental goals.

5.8. Validation

The results obtained from the experiment are aligned to the expected outcomes. This study is validated following the theoretical and conceptual parameters as proposed by Sargent (2010). Without optimization, a supply chain of this nature will naturally experience a loss because of the high transportation cost [15] Reducing these losses can be accomplished by improving the value derived from the biomass, optimizing transportation, or adjusting logistics.

The table of numerical values obtained from the simulation is presented in

Table 4. Summary of results obtained from the experiment.

Metric	KPIs	Values
Financial	Total cost (EUR) Transportation cost (EUR) Customer revenue (EUR)	5,219,411.3 5,219,411.3 14,352,654.73
Customer satisfaction	Service level (ratio)	1
Operational	Peak capacity (m3) Lead time (day) ELT service level by customer (ratio) ELT service level by order (ratio)	67 m3 8 days 1 0.1
Environmental (CO2 emission)	Total CO2 emissions (kg/m3) Number of trips	487.7 kg/m3 5678

.

By accessing and analyzing these results, the performance of the agroforestry residual biomass supply chain can be improved.

6. Discussion/Analysis

The methodological framework involved defining clear objectives for the simulation models, collecting data for each component of the supply chain, designing and structuring the model using anyLogistix, and executing and validating the simulation through a theoretical background. The GIS map marks the precise locations of customers, suppliers, and distribution centers. The visual representation aids in understanding the spatial distribution and connectivity of the supply chain. The total cost graph indicates trends in expenditure, helping to identify peak cost periods and potential areas for cost reduction. The revenue generated by customers over the simulation period reveals a steady and progressive increase in revenue, indicating growing customer orders and possibly an expanding market reach. The vehicle utilization plot is a metric that is crucial for optimizing logistics and reducing operational costs. The lead time for the delivery of the pellets was initially inconsistent; it stabilizes at around 10 days after the first 25 days. This stabilization is critical for maintaining customer satisfaction and reliable service and is an indicator that points to the fact that decisions could be made to obtain better performance. The initial 30 days show a downward trend in service level by order, which stabilizes after the first 40 days. High service levels by revenue indicate that financially significant orders are prioritized effectively. These results imply that corrective measures can be put in place to have an optimal solution through informed decision making. The carbon footprint of supply chain activities is tracked in the total CO₂ emissions graph, which highlights the environmental impact of supply chain management. The model’s accuracy in financial projections is demonstrated by the nearly similar income earned in the simulated and actual circumstances. While there is some variance between the total costs and the transportation costs, it is not very significant.

The experiment shows that the suggested paradigm works effectively and closely matches empirical facts. The little variations in lead time, mean lead time, and shipping expenses point to possible improvement areas. The model’s efficacy in terms of financial and environmental performance is validated by the consistently high service levels by revenue and precise CO₂ emission estimates. All things considered, the simulation offers an effective tool for supply chain analysis and optimization, guaranteeing dependability in practical uses.

In comparison with recent works, such as those by Zhao et al. [26] and Liu et al. [27], this study aligns with the growing emphasis on integrating sustainability into supply chain modeling. Zhao et al. [26] also employed simulation techniques to analyze CO₂ emissions, but in their work on e-commerce supply chains, they noted that adopting electric vehicles and optimizing delivery routes could drastically reduce carbon footprints, a recommendation that resonates with the findings of this study. Additionally, Liu et al. [27] explored the cost effectiveness of biomass supply chains and emphasized the importance of optimizing transportation and logistics, similarly reflected in the current work’s validation of optimization strategies for mitigating high transportation costs.

The present research extends the growing body of literature by applying these concepts specifically to agroforestry biomass supply chains. The findings validate theoretical models and reinforce the importance of continuous optimization, especially in balancing operational efficiency with environmental sustainability.

In summary, this study contributes a comprehensive framework for designing, executing, and validating simulation models of residual biomass supply chains using anyLogistix software. Key contributions include the use of GIS mapping to visualize the spatial relationships between suppliers, customers, and distribution centers, improving supply chain planning. The analysis provides insights into cost trends, revenue growth, and operational performance metrics like vehicle utilization, lead time stabilization, and service levels, offering opportunities for optimization. The model’s accuracy is validated through precise tracking of CO₂ emissions and alignment with financial projections, closely mirroring real-world conditions. This makes it a reliable tool for supply chain optimization.

6.1. Managerial Insights and Impacts of Study in the Society

The study’s practical impacts are significant, providing a route towards biomass supply chains that are more economical, ecologically friendly, and efficient. The managerial insights and societal impacts of the research are presented below:

Managerial Insights:

• Cost Management: The high total cost (EUR 5,219,411.3) identified in the study, mainly due to transportation, suggests that companies must focus on optimizing logistics. Strategies like load optimization, route management, or inventory adjustments can help reduce operational costs and improve profitability.
• Revenue Growth: The simulation shows a steady increase in revenue (EUR 14,352,654.73), which indicates that demand for biomass is growing. Managers could focus on expanding market reach while ensuring that operational costs are controlled to maximize profits.
• Logistics Optimization: The analysis of trips (5678 vehicles) and peak capacity (67 m³) highlights the importance of efficient fleet management and inventory control. Managers can use this information to optimize transport schedules and storage capacity, reducing excess trips and unnecessary inventory buildup.
• Customer Satisfaction: With the lead time stabilizing at around 10 days, it is critical to monitor and improve service levels. The study shows a service level by order ratio of 0.1 initially, indicating room for improvement in the early stages. Managers should ensure that processes are in place to meet customer expectations, especially in the first 30 days, to prevent service level dips.
• Environmental Impact: Total CO₂ emissions of 487.7 kg/m³ reveal opportunities to reduce the supply chain’s environmental footprint. Managers can adopt greener transport options and improve logistics to meet sustainability goals while complying with regulations.
• Scenario Testing: The simulation model allows decision makers to test different strategies in a controlled environment, enabling them to predict outcomes and make informed adjustments in real operations.

Societal Impacts:

• Sustainable Energy Solutions: By optimizing the biomass supply chain, this study contributes to the broader societal goal of promoting affordable and clean energy (SDG 7). Biomass as a renewable energy source becomes more viable when supply chain costs are reduced and environmental impacts are minimized.
• Environmental Sustainability: Reducing CO₂ emissions directly supports climate action (SDG 13). With improved logistics and greener transportation options, the study encourages more sustainable supply chain practices, contributing to lower greenhouse gas emissions.
• Resource Efficiency: The focus on minimizing waste and maximizing the use of residual biomass aligns with responsible consumption and production (SDG 12). Companies can adopt best practices to reduce waste, promoting a circular economy and more efficient resource utilization.

Overall, the study has important implications for both business optimization and societal sustainability efforts, emphasizing the need for efficient, cost-effective, and environmentally responsible biomass supply chains.

6.2. Recommendations for Future Work

The limitation of this research is such that it considered one case study. The simulation model may need to be expanded in future work to account for more variables, case studies, and realistic scenarios. Areas of interest for further studies are presented as follows:

• Integration with other Renewable Energy Systems: Comprehensive development of sustainable energy solutions through combination of bio-mass supply chains and other renewable energy systems such as wind and solar should be considered in future studies.
• Broad Sector and Regional Applications: Simulating biomass supply chains in different regions and sectors could help in deriving general insights that will encourage adoption of optimized biomass supply chains across the globe.

7. Conclusions

The simulation of the agroforestry residual biomass supply chain, based on the case study of Logger Y located in Portugal, provided insightful results into the operational, financial, environmental, and customer satisfaction metrics. The study revealed key performance indicators (KPIs) such as total cost, revenue by customer, number of trips, peak capacity, lead time, ELT service levels, and CO₂ emissions, all of which were crucial in evaluating the supply chain’s overall efficiency. The results indicated that transportation costs were the primary driver of the total cost, while customer revenue steadily increased over time. The lead time and service levels displayed variations but stabilized after initial fluctuations, providing actionable insights for improving operational performance.

The simulation also highlighted the environmental impact of CO₂ emissions, where a significant amount of carbon was emitted, suggesting the need for greener logistics solutions. Furthermore, the experiment’s validation confirmed that without optimization, supply chains such as this would suffer losses due to high transportation costs. The study provides a robust framework for decision makers to enhance supply chain performance through better logistics management, optimizing transportation, and increasing the value of residual biomass. The major contributions are listed below:

• Comprehensive KPI Analysis: The study provided a detailed examination of key performance indicators, including financial, operational, environmental, and customer satisfaction metrics. This offers a holistic view of supply chain performance.
• Supply Chain Simulation: The use of anyLogistix and agent-based modeling for simulating the agroforestry residual biomass supply chain provided a practical demonstration of how supply chain elements interact in real time, aiding in decision making.
• Environmental Impact Assessment: The analysis of CO₂ emissions and suggestions for reducing greenhouse gases by adopting environmentally friendly transportation highlighted the study’s contribution toward sustainability in supply chain operations.
• Optimization Insights: The results emphasized the need for optimizing logistics, particularly transportation, to reduce costs and environmental impact, aligning with the sustainable development goals.
• Validation of Theoretical Models: The simulation results validated theoretical models, confirming that optimization of transportation and logistics would significantly improve supply chain efficiency and reduce financial losses.

This research contributes to the body of knowledge in supply chain management, particularly for industries handling residual biomass, by offering strategies for improving operational efficiency and environmental sustainability.